Technical progress in regard to radar has made it possible to progress toward the realization of multichannel radars, that is to say radars using several antenna sub-arrays. This type of radar makes it possible notably to carry out computational beamforming and offers better antijamming capability than single-antenna radars. Generally, the antijamming systems associated with multichannel radars apply a weighting coefficient wi to each of the signals xi arising from the N antenna sub-arrays before combining them to form a “sum channel” y according to the following formulation:
                              y          =                                                    w                H                            ·                              x                received                                      =                                                            ∑                                      i                    =                    1                                    N                                ⁢                                                                  ⁢                                                      wi                    H                                    ·                  xi                                            =                                                ∑                                      i                    =                    1                                    N                                ⁢                                                                  ⁢                yi                                                    ,                            (        1        )            xreceived being the vector formed of the N signals xi,( )H being the Hermitian operator.
The filtering vector w containing the Weighting coefficients wi is determined by an antijamming algorithm with a view to decreasing the gain of the antenna in the directions of arrival of the undesirable signals, while complying with a certain number of constraints making it possible to preserve good reception of the useful signal.
A major drawback of this antijamming method is the loss of the radar's multichannel characteristic, since only the sum channel y is cleaned. The signals yi arising from each weighting wi.xi of the antenna sub-arrays are not separately reusable, thus depriving the user of the radar of the numerous possibilities of spatio-temporal adaptive processings, applicable notably to the detection of moving targets and to synthetic aperture radar imaging. Moreover, from the point of view of the architecture of a system and more particularly of its interfaces, this antijamming method is not transparent since it transforms a set of N signals into a single output signal. The implementation of such a method within an existing multichannel radar reception chain may notably restrict the possibilities for adding downstream utilization devices since it modifies the number of outputs.
Procedures are known for producing at the output of an antijamming device as many cleaned channels as antenna sub-arrays. This involves computing according to an iterative algorithm the weighting coefficients of each of the cleaned channels. However, real-time operating constraints often being needed on radar systems, this procedure requires considerable computational means and is consequently not compatible with the hardware limits inherent in onboard systems.
In the case of conventional antijamming on a radar system with N antenna sub-arrays, it is possible, on the basis of a noise covariance matrix Γ of dimension N×N, to determine a filtering vector w making it possible to reduce the gain of the antenna in the directions of the jammer signals. The covariance matrix Γ is for example established during a phase of listening without prior emission of pulses by the radar. The power P0 received by the radar after weighting the channels of the sub-arrays with the filtering vector w is then:P0=w·Γ·w 
It is then possible to use the “Linear Constraint Minimum Variance” or LCMV algorithm, proposed by Frost in 1972, to minimize this output power P0 while preserving an antenna pattern allowing satisfactory reception of the useful signals. Thus, the search for the filtering vector w can be formulated as a constrained minimization problem expressed in the following form:
  {                                                        P              0                        =                                          min                w                            ⁢                              (                                  w                  ·                  Γ                  ·                  w                                )                                                                          with                                                                                C                H                            ·              w                        =            f                                   
where the matrix C of dimension N×nc represents the nc constraints to be satisfied so as to preserve the antenna pattern, the vector f of dimension nc contains the constraint values. The constraints may notably result in the conservation of a sufficient gain in the direction of observation of the radar and in the control of the level of the sidelobes of the antenna pattern. The objective of the antijamming processing being to increase the signal-to-noise ratio of the useful signal, it is in fact necessary to take care to decrease the amplitude of the jammer signals without altering the reception of the sought-after signals.
The solution obtained by using Lagrange multipliers gives:w=Γ−1·C·(CH·Γ−1·C)−1·f   (2)that is to say, by applying the Hermitian operator:wH=fH·(CH·Γ−1·C)−1·CH·Γ−1   (3)Expression (3) is a filtering vector wH known to the person skilled in the art for applying a conventional antijamming processing.